DocumentCode
394425
Title
Optimising a stochastic dynamic neural tree
Author
Pensuwon, W. ; Adams, R.G. ; Davey, N.
Author_Institution
Dept. of Comput. Sci., Hertfordshire Univ., Hatfield, UK
Volume
4
fYear
2002
fDate
18-22 Nov. 2002
Firstpage
1926
Abstract
This paper describes experiments performed using a genetic algorithm (GA) to optimise the parameters of a novel model of a stochastic hierarchical neural clusterer. Two issues of enhancing and optimising the model are discussed. Two fitness functions were created from two selected clustering measures, and a population of genotypes, specifying parameters of the model were evolved. Using the idea of optimising the model by a GA has been proven to be useful. This process mirrors genomic evolution and ontogeny.
Keywords
genetic algorithms; neural net architecture; pattern clustering; trees (mathematics); experiments; fitness functions; genetic algorithm; genomic evolution; genotypes; ontogeny; stochastic dynamic neural tree optimisation; stochastic hierarchical neural clusterer; Bioinformatics; Clustering algorithms; Computer science; Counting circuits; Genetic algorithms; Genetic engineering; Genomics; Mirrors; Stochastic processes; Tree data structures;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN
981-04-7524-1
Type
conf
DOI
10.1109/ICONIP.2002.1199009
Filename
1199009
Link To Document